Surveillance systems and related item tracking technologies are designed to detect moving targets within one or more areas, and to thereby characterize the targets and/or their movements, or otherwise derive information related thereto. Thus, such surveillance and tracking systems may be used, for example, to track persons moving within a physical location (e.g., an airport, or a retail establishment), or may be used to monitor traffic moving within the streets of a city.
In these and many other example scenarios, a plurality of sensors may be deployed within and/or around a defined area(s), and the sensors may be operable to detect targets, movements, and other related data. Then, the data collected from the various sensors may be combined and utilized to determine desired information related to specific targets in the area. For example, in the above example scenarios, sensors deployed around an airport may provide sensor data with respect to persons and luggage within the airport, and the acquired data from the sensors may be processed in order to identify a particular person and/or activity which may be associated with a terrorist attack or other security breach at the airport. Similarly, sensors deployed at a retail establishment may be utilized to detect possible theft of merchandise, while traffic sensors may be utilized to determine traffic violators, or the existence and potential causes of traffic congestion.
In many such scenarios, sensor data provided by deployed sensors for processing may be unsynchronized, voluminous, and high variable. As a result, surveillance and tracking systems which receive such sensor data may experience correspondingly high, variable, and unpredictable computational loads.
Consequently, it is difficult for conventional surveillance and tracking systems to provide accurate, timely results in the presence of variable, high computational loads, in a manner that is practical and cost-effective.